Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow #766

Closed
Closed
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow#766
Assignees
jpn--
Labels
BugSomething isn't working/bug f

Description

@i-am-sijia
i-am-sijia
opened on Dec 15, 2023

Describe the bug
After implementing the string to pandas categorical conversion, some of our current CI tests failed. They all had Sharrow turned on and set to test mode. The utility calculated with and without Sharrow are different.

[48:39.10] INFO: completedflow_LQLDEWSFEGQ5W2NJNONCWPNFSCB7O5RD.loadin0:00:18.710844stop_frequency.work.simple_simulate.eval_mnl
[48:39.10] INFO: completedapply_flowin0:00:21.651810 [48:39.10] INFO: elapsedtimesharrowflow0:00:21.659376stop_frequency.work.simple_simulate.eval_mnl
[48:39.31] INFO: elapsedtimesimpleflow0:00:00.207622stop_frequency.work.simple_simulate.eval_mnl.eval_utilsNotequaltotolerancertol=0.01, atol=0utilitynotalignedMismatchedelements: 132/144 (91.7%)
Maxabsolutedifference: 1998.00011762Maxrelativedifference: 1729.2712081x: array([[ 0. , -1000.9582 , -1002.2882 , -1002.6522 , -1001.3462 ,
-2000.7913 , -2002.1212 , -2002.4852 , -1003.1262 , -2002.5713 ,
-2003.9012 , -2003.5703 , -1004.4472 , -2003.8922 , -2004.5272 ,...
y: array([[ 0.000000e+00, -1.958200e+00, -3.288200e+00, -3.652200e+00,
-2.346200e+00, -2.791200e+00, -4.121200e+00, -4.485200e+00,
-4.126200e+00, -4.571200e+00, -5.901200e+00, -5.570200e+00,...
bigproblem: 132missedclosevaluesoutof144 (91.67%)
sh_util.shape=(9, 16)
(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 5, 6, 7,
9, 10, 11, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))
possibleproblematicexpressions:
11.1% [043] (school_esc_outbound.isin(['ride_share', 'pure_escort']))
00.0% [044] (school_esc_inbound.isin(['ride_share', 'pure_escort']))
[48:43.24] ERROR: =====ERRORINstop_frequency=====
[48:43.24] ERROR: Notequaltotolerancertol=0.01, atol=0utilitynotaligned

To Reproduce
Steps to reproduce the behavior:

  1. Check out ...
  2. Run test_mtc_extended.py::test_prototype_mtc_extended_sharrow()

Expected behavior
The utility with and with Sharrow should be the same.

Screenshots
result of tracing the failed tour, in the stop_frequency.work:
Chooser
image

Non-sharrow evaluation
image

Sharrow evaluation]
image

Additional context
Temporary solution: I moved the pandas categorical vs string comparisons to the preprocessors.

Metadata

Metadata

Assignees

Labels

BugSomething isn't working/bug f

Type

No type

Projects

Milestone

No milestone

Relationships

None yet

Development

No branches or pull requests

Issue actions

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow #766
Closed
Closed
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow#766
Assignees
jpn--
Labels
BugSomething isn't working/bug f
Description
@i-am-sijia
i-am-sijia
opened on Dec 15, 2023
Describe the bug

After implementing the string to pandas categorical conversion, some of our current CI tests failed. They all had Sharrow turned on and set to test mode. The utility calculated with and without Sharrow are different.
[48:39.10] INFO: completedflow_LQLDEWSFEGQ5W2NJNONCWPNFSCB7O5RD.loadin0:00:18.710844stop_frequency.work.simple_simulate.eval_mnl
[48:39.10] INFO: completedapply_flowin0:00:21.651810 [48:39.10] INFO: elapsedtimesharrowflow0:00:21.659376stop_frequency.work.simple_simulate.eval_mnl
[48:39.31] INFO: elapsedtimesimpleflow0:00:00.207622stop_frequency.work.simple_simulate.eval_mnl.eval_utilsNotequaltotolerancertol=0.01, atol=0utilitynotalignedMismatchedelements: 132/144 (91.7%)
Maxabsolutedifference: 1998.00011762Maxrelativedifference: 1729.2712081x: array([[ 0. , -1000.9582 , -1002.2882 , -1002.6522 , -1001.3462 ,
-2000.7913 , -2002.1212 , -2002.4852 , -1003.1262 , -2002.5713 ,
-2003.9012 , -2003.5703 , -1004.4472 , -2003.8922 , -2004.5272 ,...
y: array([[ 0.000000e+00, -1.958200e+00, -3.288200e+00, -3.652200e+00,
-2.346200e+00, -2.791200e+00, -4.121200e+00, -4.485200e+00,
-4.126200e+00, -4.571200e+00, -5.901200e+00, -5.570200e+00,...
bigproblem: 132missedclosevaluesoutof144 (91.67%)
sh_util.shape=(9, 16)
(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 5, 6, 7,
9, 10, 11, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))
possibleproblematicexpressions:
11.1% [043] (school_esc_outbound.isin(['ride_share', 'pure_escort']))
00.0% [044] (school_esc_inbound.isin(['ride_share', 'pure_escort']))
[48:43.24] ERROR: =====ERRORINstop_frequency=====
[48:43.24] ERROR: Notequaltotolerancertol=0.01, atol=0utilitynotaligned
To Reproduce

Steps to reproduce the behavior:
  1. Check out ...
  2. Run test_mtc_extended.py::test_prototype_mtc_extended_sharrow()
Expected behavior

The utility with and with Sharrow should be the same.
Screenshots

result of tracing the failed tour, in the stop_frequency.work:

Chooser
image
Non-sharrow evaluation
image
Sharrow evaluation]
image
Additional context

Temporary solution: I moved the pandas categorical vs string comparisons to the preprocessors.
Metadata
Metadata
Assignees
Labels
BugSomething isn't working/bug f
Type
No type
Projects
Milestone
No milestone
Relationships
None yet
Development
No branches or pull requests
Issue actions

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow #766
Closed
Closed
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow#766
Assignees
jpn--
Labels
BugSomething isn't working/bug f
Description
@i-am-sijia
i-am-sijia
opened on Dec 15, 2023
Describe the bug

After implementing the string to pandas categorical conversion, some of our current CI tests failed. They all had Sharrow turned on and set to test mode. The utility calculated with and without Sharrow are different.
[48:39.10] INFO: completedflow_LQLDEWSFEGQ5W2NJNONCWPNFSCB7O5RD.loadin0:00:18.710844stop_frequency.work.simple_simulate.eval_mnl
[48:39.10] INFO: completedapply_flowin0:00:21.651810 [48:39.10] INFO: elapsedtimesharrowflow0:00:21.659376stop_frequency.work.simple_simulate.eval_mnl
[48:39.31] INFO: elapsedtimesimpleflow0:00:00.207622stop_frequency.work.simple_simulate.eval_mnl.eval_utilsNotequaltotolerancertol=0.01, atol=0utilitynotalignedMismatchedelements: 132/144 (91.7%)
Maxabsolutedifference: 1998.00011762Maxrelativedifference: 1729.2712081x: array([[ 0. , -1000.9582 , -1002.2882 , -1002.6522 , -1001.3462 ,
-2000.7913 , -2002.1212 , -2002.4852 , -1003.1262 , -2002.5713 ,
-2003.9012 , -2003.5703 , -1004.4472 , -2003.8922 , -2004.5272 ,...
y: array([[ 0.000000e+00, -1.958200e+00, -3.288200e+00, -3.652200e+00,
-2.346200e+00, -2.791200e+00, -4.121200e+00, -4.485200e+00,
-4.126200e+00, -4.571200e+00, -5.901200e+00, -5.570200e+00,...
bigproblem: 132missedclosevaluesoutof144 (91.67%)
sh_util.shape=(9, 16)
(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 5, 6, 7,
9, 10, 11, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))
possibleproblematicexpressions:
11.1% [043] (school_esc_outbound.isin(['ride_share', 'pure_escort']))
00.0% [044] (school_esc_inbound.isin(['ride_share', 'pure_escort']))
[48:43.24] ERROR: =====ERRORINstop_frequency=====
[48:43.24] ERROR: Notequaltotolerancertol=0.01, atol=0utilitynotaligned
To Reproduce

Steps to reproduce the behavior:
  1. Check out ...
  2. Run test_mtc_extended.py::test_prototype_mtc_extended_sharrow()
Expected behavior

The utility with and with Sharrow should be the same.
Screenshots

result of tracing the failed tour, in the stop_frequency.work:

Chooser
image
Non-sharrow evaluation
image
Sharrow evaluation]
image
Additional context

Temporary solution: I moved the pandas categorical vs string comparisons to the preprocessors.
Metadata
Metadata
Assignees
Labels
BugSomething isn't working/bug f
Type
No type
Projects
Milestone
No milestone
Relationships
None yet
Development
No branches or pull requests
Issue actions

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow #766
Closed
Closed
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow#766
Assignees
jpn--
Labels
BugSomething isn't working/bug f
Description
@i-am-sijia
i-am-sijia
opened on Dec 15, 2023
Describe the bug

After implementing the string to pandas categorical conversion, some of our current CI tests failed. They all had Sharrow turned on and set to test mode. The utility calculated with and without Sharrow are different.
[48:39.10] INFO: completedflow_LQLDEWSFEGQ5W2NJNONCWPNFSCB7O5RD.loadin0:00:18.710844stop_frequency.work.simple_simulate.eval_mnl
[48:39.10] INFO: completedapply_flowin0:00:21.651810 [48:39.10] INFO: elapsedtimesharrowflow0:00:21.659376stop_frequency.work.simple_simulate.eval_mnl
[48:39.31] INFO: elapsedtimesimpleflow0:00:00.207622stop_frequency.work.simple_simulate.eval_mnl.eval_utilsNotequaltotolerancertol=0.01, atol=0utilitynotalignedMismatchedelements: 132/144 (91.7%)
Maxabsolutedifference: 1998.00011762Maxrelativedifference: 1729.2712081x: array([[ 0. , -1000.9582 , -1002.2882 , -1002.6522 , -1001.3462 ,
-2000.7913 , -2002.1212 , -2002.4852 , -1003.1262 , -2002.5713 ,
-2003.9012 , -2003.5703 , -1004.4472 , -2003.8922 , -2004.5272 ,...
y: array([[ 0.000000e+00, -1.958200e+00, -3.288200e+00, -3.652200e+00,
-2.346200e+00, -2.791200e+00, -4.121200e+00, -4.485200e+00,
-4.126200e+00, -4.571200e+00, -5.901200e+00, -5.570200e+00,...
bigproblem: 132missedclosevaluesoutof144 (91.67%)
sh_util.shape=(9, 16)
(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 5, 6, 7,
9, 10, 11, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))
possibleproblematicexpressions:
11.1% [043] (school_esc_outbound.isin(['ride_share', 'pure_escort']))
00.0% [044] (school_esc_inbound.isin(['ride_share', 'pure_escort']))
[48:43.24] ERROR: =====ERRORINstop_frequency=====
[48:43.24] ERROR: Notequaltotolerancertol=0.01, atol=0utilitynotaligned
To Reproduce

Steps to reproduce the behavior:
  1. Check out ...
  2. Run test_mtc_extended.py::test_prototype_mtc_extended_sharrow()
Expected behavior

The utility with and with Sharrow should be the same.
Screenshots

result of tracing the failed tour, in the stop_frequency.work:

Chooser
image
Non-sharrow evaluation
image
Sharrow evaluation]
image
Additional context

Temporary solution: I moved the pandas categorical vs string comparisons to the preprocessors.
Metadata
Metadata
Assignees
Labels
BugSomething isn't working/bug f
Type
No type
Projects
Milestone
No milestone
Relationships
None yet
Development
No branches or pull requests
Issue actions

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow #766
Closed
Closed
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow#766
Assignees
jpn--
Labels
BugSomething isn't working/bug f
Description
@i-am-sijia
i-am-sijia
opened on Dec 15, 2023
Describe the bug

After implementing the string to pandas categorical conversion, some of our current CI tests failed. They all had Sharrow turned on and set to test mode. The utility calculated with and without Sharrow are different.
[48:39.10] INFO: completedflow_LQLDEWSFEGQ5W2NJNONCWPNFSCB7O5RD.loadin0:00:18.710844stop_frequency.work.simple_simulate.eval_mnl
[48:39.10] INFO: completedapply_flowin0:00:21.651810 [48:39.10] INFO: elapsedtimesharrowflow0:00:21.659376stop_frequency.work.simple_simulate.eval_mnl
[48:39.31] INFO: elapsedtimesimpleflow0:00:00.207622stop_frequency.work.simple_simulate.eval_mnl.eval_utilsNotequaltotolerancertol=0.01, atol=0utilitynotalignedMismatchedelements: 132/144 (91.7%)
Maxabsolutedifference: 1998.00011762Maxrelativedifference: 1729.2712081x: array([[ 0. , -1000.9582 , -1002.2882 , -1002.6522 , -1001.3462 ,
-2000.7913 , -2002.1212 , -2002.4852 , -1003.1262 , -2002.5713 ,
-2003.9012 , -2003.5703 , -1004.4472 , -2003.8922 , -2004.5272 ,...
y: array([[ 0.000000e+00, -1.958200e+00, -3.288200e+00, -3.652200e+00,
-2.346200e+00, -2.791200e+00, -4.121200e+00, -4.485200e+00,
-4.126200e+00, -4.571200e+00, -5.901200e+00, -5.570200e+00,...
bigproblem: 132missedclosevaluesoutof144 (91.67%)
sh_util.shape=(9, 16)
(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 5, 6, 7,
9, 10, 11, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))
possibleproblematicexpressions:
11.1% [043] (school_esc_outbound.isin(['ride_share', 'pure_escort']))
00.0% [044] (school_esc_inbound.isin(['ride_share', 'pure_escort']))
[48:43.24] ERROR: =====ERRORINstop_frequency=====
[48:43.24] ERROR: Notequaltotolerancertol=0.01, atol=0utilitynotaligned
To Reproduce

Steps to reproduce the behavior:
  1. Check out ...
  2. Run test_mtc_extended.py::test_prototype_mtc_extended_sharrow()
Expected behavior

The utility with and with Sharrow should be the same.
Screenshots

result of tracing the failed tour, in the stop_frequency.work:

Chooser
image
Non-sharrow evaluation
image
Sharrow evaluation]
image
Additional context

Temporary solution: I moved the pandas categorical vs string comparisons to the preprocessors.
Metadata
Metadata
Assignees
Labels
BugSomething isn't working/bug f
Type
No type
Projects
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No milestone
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No branches or pull requests
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow #766
Closed
Closed
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow#766
Assignees
jpn--
Labels
BugSomething isn't working/bug f
Description
@i-am-sijia
i-am-sijia
opened on Dec 15, 2023
Describe the bug

After implementing the string to pandas categorical conversion, some of our current CI tests failed. They all had Sharrow turned on and set to test mode. The utility calculated with and without Sharrow are different.
[48:39.10] INFO: completedflow_LQLDEWSFEGQ5W2NJNONCWPNFSCB7O5RD.loadin0:00:18.710844stop_frequency.work.simple_simulate.eval_mnl
[48:39.10] INFO: completedapply_flowin0:00:21.651810 [48:39.10] INFO: elapsedtimesharrowflow0:00:21.659376stop_frequency.work.simple_simulate.eval_mnl
[48:39.31] INFO: elapsedtimesimpleflow0:00:00.207622stop_frequency.work.simple_simulate.eval_mnl.eval_utilsNotequaltotolerancertol=0.01, atol=0utilitynotalignedMismatchedelements: 132/144 (91.7%)
Maxabsolutedifference: 1998.00011762Maxrelativedifference: 1729.2712081x: array([[ 0. , -1000.9582 , -1002.2882 , -1002.6522 , -1001.3462 ,
-2000.7913 , -2002.1212 , -2002.4852 , -1003.1262 , -2002.5713 ,
-2003.9012 , -2003.5703 , -1004.4472 , -2003.8922 , -2004.5272 ,...
y: array([[ 0.000000e+00, -1.958200e+00, -3.288200e+00, -3.652200e+00,
-2.346200e+00, -2.791200e+00, -4.121200e+00, -4.485200e+00,
-4.126200e+00, -4.571200e+00, -5.901200e+00, -5.570200e+00,...
bigproblem: 132missedclosevaluesoutof144 (91.67%)
sh_util.shape=(9, 16)
(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 5, 6, 7,
9, 10, 11, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))
possibleproblematicexpressions:
11.1% [043] (school_esc_outbound.isin(['ride_share', 'pure_escort']))
00.0% [044] (school_esc_inbound.isin(['ride_share', 'pure_escort']))
[48:43.24] ERROR: =====ERRORINstop_frequency=====
[48:43.24] ERROR: Notequaltotolerancertol=0.01, atol=0utilitynotaligned
To Reproduce

Steps to reproduce the behavior:
  1. Check out ...
  2. Run test_mtc_extended.py::test_prototype_mtc_extended_sharrow()
Expected behavior

The utility with and with Sharrow should be the same.
Screenshots

result of tracing the failed tour, in the stop_frequency.work:

Chooser
image
Non-sharrow evaluation
image
Sharrow evaluation]
image
Additional context

Temporary solution: I moved the pandas categorical vs string comparisons to the preprocessors.
Metadata
Metadata
Assignees
Labels
BugSomething isn't working/bug f
Type
No type
Projects
Milestone
No milestone
Relationships
None yet
Development
No branches or pull requests
Issue actions

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow #766
Closed
Closed
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow#766
Assignees
jpn--
Labels
BugSomething isn't working/bug f
Description
@i-am-sijia
i-am-sijia
opened on Dec 15, 2023
Describe the bug

After implementing the string to pandas categorical conversion, some of our current CI tests failed. They all had Sharrow turned on and set to test mode. The utility calculated with and without Sharrow are different.
[48:39.10] INFO: completedflow_LQLDEWSFEGQ5W2NJNONCWPNFSCB7O5RD.loadin0:00:18.710844stop_frequency.work.simple_simulate.eval_mnl
[48:39.10] INFO: completedapply_flowin0:00:21.651810 [48:39.10] INFO: elapsedtimesharrowflow0:00:21.659376stop_frequency.work.simple_simulate.eval_mnl
[48:39.31] INFO: elapsedtimesimpleflow0:00:00.207622stop_frequency.work.simple_simulate.eval_mnl.eval_utilsNotequaltotolerancertol=0.01, atol=0utilitynotalignedMismatchedelements: 132/144 (91.7%)
Maxabsolutedifference: 1998.00011762Maxrelativedifference: 1729.2712081x: array([[ 0. , -1000.9582 , -1002.2882 , -1002.6522 , -1001.3462 ,
-2000.7913 , -2002.1212 , -2002.4852 , -1003.1262 , -2002.5713 ,
-2003.9012 , -2003.5703 , -1004.4472 , -2003.8922 , -2004.5272 ,...
y: array([[ 0.000000e+00, -1.958200e+00, -3.288200e+00, -3.652200e+00,
-2.346200e+00, -2.791200e+00, -4.121200e+00, -4.485200e+00,
-4.126200e+00, -4.571200e+00, -5.901200e+00, -5.570200e+00,...
bigproblem: 132missedclosevaluesoutof144 (91.67%)
sh_util.shape=(9, 16)
(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 5, 6, 7,
9, 10, 11, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))
possibleproblematicexpressions:
11.1% [043] (school_esc_outbound.isin(['ride_share', 'pure_escort']))
00.0% [044] (school_esc_inbound.isin(['ride_share', 'pure_escort']))
[48:43.24] ERROR: =====ERRORINstop_frequency=====
[48:43.24] ERROR: Notequaltotolerancertol=0.01, atol=0utilitynotaligned
To Reproduce

Steps to reproduce the behavior:
  1. Check out ...
  2. Run test_mtc_extended.py::test_prototype_mtc_extended_sharrow()
Expected behavior

The utility with and with Sharrow should be the same.
Screenshots

result of tracing the failed tour, in the stop_frequency.work:

Chooser
image
Non-sharrow evaluation
image
Sharrow evaluation]
image
Additional context

Temporary solution: I moved the pandas categorical vs string comparisons to the preprocessors.
Metadata
Metadata
Assignees
Labels
BugSomething isn't working/bug f
Type
No type
Projects
Milestone
No milestone
Relationships
None yet
Development
No branches or pull requests
Issue actions

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow #766
Closed
Closed
Utility terms that compare pandas categorical variable to strings are not evaluated correctly with Sharrow#766
Assignees
jpn--
Labels
BugSomething isn't working/bug f
Description
@i-am-sijia
i-am-sijia
opened on Dec 15, 2023
Describe the bug

After implementing the string to pandas categorical conversion, some of our current CI tests failed. They all had Sharrow turned on and set to test mode. The utility calculated with and without Sharrow are different.
[48:39.10] INFO: completedflow_LQLDEWSFEGQ5W2NJNONCWPNFSCB7O5RD.loadin0:00:18.710844stop_frequency.work.simple_simulate.eval_mnl
[48:39.10] INFO: completedapply_flowin0:00:21.651810 [48:39.10] INFO: elapsedtimesharrowflow0:00:21.659376stop_frequency.work.simple_simulate.eval_mnl
[48:39.31] INFO: elapsedtimesimpleflow0:00:00.207622stop_frequency.work.simple_simulate.eval_mnl.eval_utilsNotequaltotolerancertol=0.01, atol=0utilitynotalignedMismatchedelements: 132/144 (91.7%)
Maxabsolutedifference: 1998.00011762Maxrelativedifference: 1729.2712081x: array([[ 0. , -1000.9582 , -1002.2882 , -1002.6522 , -1001.3462 ,
-2000.7913 , -2002.1212 , -2002.4852 , -1003.1262 , -2002.5713 ,
-2003.9012 , -2003.5703 , -1004.4472 , -2003.8922 , -2004.5272 ,...
y: array([[ 0.000000e+00, -1.958200e+00, -3.288200e+00, -3.652200e+00,
-2.346200e+00, -2.791200e+00, -4.121200e+00, -4.485200e+00,
-4.126200e+00, -4.571200e+00, -5.901200e+00, -5.570200e+00,...
bigproblem: 132missedclosevaluesoutof144 (91.67%)
sh_util.shape=(9, 16)
(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2, 3, 5, 6, 7,
9, 10, 11, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]))
possibleproblematicexpressions:
11.1% [043] (school_esc_outbound.isin(['ride_share', 'pure_escort']))
00.0% [044] (school_esc_inbound.isin(['ride_share', 'pure_escort']))
[48:43.24] ERROR: =====ERRORINstop_frequency=====
[48:43.24] ERROR: Notequaltotolerancertol=0.01, atol=0utilitynotaligned
To Reproduce

Steps to reproduce the behavior:
  1. Check out ...
  2. Run test_mtc_extended.py::test_prototype_mtc_extended_sharrow()
Expected behavior

The utility with and with Sharrow should be the same.
Screenshots

result of tracing the failed tour, in the stop_frequency.work:

Chooser
image
Non-sharrow evaluation
image
Sharrow evaluation]
image
Additional context

Temporary solution: I moved the pandas categorical vs string comparisons to the preprocessors.
Metadata
Metadata
Assignees
Labels
BugSomething isn't working/bug f
Type
No type
Projects
Milestone
No milestone
Relationships
None yet
Development
No branches or pull requests
Issue actions